GPU-based efficient join algorithms on Hadoop - Wang, Hongzhi; Li, Ning; Wang, Zheng; Li, Jianing (The Journal of supercomputing, 2021)
The growing data have brought tremendous pressure for query processing and storage, so there are many studies that focus on using GPU to accelerate join operation, which is one of the most important operations in modern database systems. However, existing GPU acceleration join operation researches are not very suitable for the join operation on big data. Based on this, this paper speeds up nested loop join, hash join and theta join, combining Hadoop with GPU, which is also the first to use GPU to accelerate theta join. At the same time, after the data pre-filtering and pre-processing, using MapReduce and HDFS in Hadoop proposed in this paper, the larger data table can be handled, compared to existing GPU acceleration methods. Also with MapReduce in Hadoop, the algorithm proposed in this paper can estimate the number of results more accurately and allocate the appropriate storage space without unnecessary costs, making it more efficient. Experimental results show that comparing with GPU-based approach without Hadoop, our approach increases the speed by 1.5–2 times, and comparing with the Hadoop-based approaches without GPU, our approach increases the speed by 1.3–2 times.